CIO

JANUARY 6, 2023

These candidates should have experience debugging cloud stacks, securing apps in the cloud, and creating cloud-based solutions. Cloud engineers should have experience troubleshooting, analytical skills, and knowledge of SysOps, Azure, AWS, GCP, and CI/CD systems.

LAN 358

LAN Systems Administration How To WAN 358

Mobilunity

AUGUST 3, 2021

The cloud offers excellent scalability, while graph databases offer the ability to display incredible amounts of data in a way that makes analytics efficient and effective. Who is Big Data Engineer? Big Data requires a unique engineering approach. Big Data Engineer vs Data Scientist.

Big Data 52

Big Data Data Engineering Engineering Data 52

Xebia

FEBRUARY 25, 2024

Data science is generally not operationalized Consider a data flow from a machine or process, all the way to an end-user. 2 In general, the flow of data from machine to the data engineer (1) is well operationalized. You could argue the same about the data engineering step (2) , although this differs per company.

Applications 130

Applications Technical Review Software Review Azure 130

O'Reilly Media - Ideas

JANUARY 29, 2019

These include data integration and extract, transform, and load (ETL) (60% of respondents indicated they were building or evaluating solutions), data preparation and cleaning (52%), data governance (31%), metadata analysis and management (28%), and data lineage management (21%).

Sustainability 85

Sustainability Company Machine Learning Artificial Inteligence 85

Hu's Place - HitachiVantara

NOVEMBER 11, 2018

Forbes notes that a full transition to the cloud has proved more challenging than anticipated and many companies will use hybrid cloud solutions to transition to the cloud at their own pace and at a lower risk and cost. This will be a blend of private and public hyperscale clouds like AWS, Azure, and Google Cloud Platform.

Cloud 78

Cloud Google Cloud Azure AWS 78

Altexsoft

JUNE 29, 2021

MLEs are usually a part of a data science team which includes data engineers , data architects, data and business analysts, and data scientists. Who does what in a data science team. Machine learning engineers are relatively new to data-driven companies.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

O'Reilly Media - Ideas

JUNE 4, 2019

AWS Security Fundamentals , July 15. AWS Certified Security - Specialty Crash Course , July 25-26. Systems engineering and operations. AWS Access Management , June 6. Google Cloud Platform – Professional Cloud Developer Crash Course , June 6-7. Getting Started with Google Cloud Platform , June 24.

Course 64

Course Training Artificial Inteligence Software Review 64

ParkMyCloud

JANUARY 17, 2020

Each of the ‘big three’ cloud providers (AWS, Azure, GCP) offer a number of cloud certification options that individuals can get to validate their cloud knowledge and skill set, while helping them advance in their careers and broaden the scope of their achievements. . Amazon Web Services (AWS) Certifications.

Google Cloud 9

Google Cloud Azure AWS Cloud 9

O'Reilly Media - Ideas

JUNE 5, 2019

AWS Security Fundamentals , July 15. AWS Certified Security - Specialty Crash Course , July 25-26. Systems engineering and operations. AWS Access Management , June 6. Google Cloud Platform – Professional Cloud Developer Crash Course , June 6-7. Getting Started with Google Cloud Platform , June 24.

Course 49

Course Training Artificial Inteligence Software Review 49

O'Reilly Media - Ideas

MARCH 1, 2023

The first quantum computers are now available through cloud providers like IBM and Amazon Web Services (AWS). Data Data is another very broad category, encompassing everything from traditional business analytics to artificial intelligence. Data engineering was the dominant topic by far, growing 35% year over year.

Trends 133

Trends Technical Review Technology Software Review 133

Altexsoft

JANUARY 14, 2021

What specialists and their expertise level are required to handle a data warehouse? However, all of the warehouse products available require some technical expertise to run, including data engineering and, in some cases, DevOps. The comparison of the top data warehouse software products. Data loading. Source: AWS.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

DECEMBER 4, 2020

Three types of data migration tools. Automation scripts can be written by data engineers or ETL developers in charge of your migration project. This makes sense when you move a relatively small amount of data and deal with simple requirements. Phases of the data migration process. Data sources and destinations.

Software Review 69

Software Review Software Data Technical Review 69

Capgemini

JANUARY 17, 2023

By creating a lakehouse, a company gives every employee the ability to access and employ data and artificial intelligence to make better business decisions. Many organizations that implement a lakehouse as their key data strategy are seeing lightning-speed data insights with horizontally scalable data-engineering pipelines.

Strategy 52

Strategy Technical Review Data Artificial Inteligence 52

Exadel

FEBRUARY 20, 2023

Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

O'Reilly Media - Ideas

JANUARY 25, 2024

Before that, cloud computing itself took off in roughly 2010 (AWS was founded in 2006); and Agile goes back to 2000 (the Agile Manifesto dates back to 2001, Extreme Programming to 1999). Data analysis and databases Data engineering was by far the most heavily used topic in this category; it showed a 3.6%

Trends 115

Trends Technical Review Technology Artificial Inteligence 115

Confluent

FEBRUARY 6, 2019

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Impedance mismatch between data scientists, data engineers and production engineers. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Altexsoft

APRIL 26, 2022

The platform provides fast, flexible, and easy-to-use options for data storage, processing, and analysis. Initially built on top of the Amazon Web Services (AWS), Snowflake is also available on Google Cloud and Microsoft Azure. As such, it is considered cloud-agnostic. Source: Snowflake. Query processing layer.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

APRIL 16, 2021

An overview of data warehouse types. Optionally, you may study some basic terminology on data engineering or watch our short video on the topic: What is data engineering. What is data pipeline. Creating a cube is a custom process each time, because data can’t be updated once it was modeled in a cube.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

Altexsoft

JULY 23, 2020

It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development. MLOps lies at the confluence of ML, data engineering, and DevOps.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

O'Reilly Media - Ideas

SEPTEMBER 15, 2021

Cloud certifications, specifically in AWS and Microsoft Azure, were most strongly associated with salary increases. As we’ll see later, cloud certifications (specifically in AWS and Microsoft Azure) were the most popular and appeared to have the largest effect on salaries. The top certification was for AWS (3.9%

Survey 145

Survey Data Technical Review Training 145

O'Reilly Media - Ideas

MAY 1, 2019

Fundamentals of Machine Learning with AWS , June 19. Building Machine Learning Models with AWS Sagemaker , June 20. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. AWS CloudFormation Deep Dive , June 3-4.

Course 45

Course Training Artificial Inteligence Machine Learning 45

O'Reilly Media - Ideas

JANUARY 25, 2022

A quick look at bigram usage (word pairs) doesn’t really distinguish between “data science,” “data engineering,” “data analysis,” and other terms; the most common word pair with “data” is “data governance,” followed by “data science.” That’s not surprising. We also see another important trend.

Trends 109

Trends Technical Review Technology Artificial Inteligence 109

UruIT

APRIL 27, 2020

Google Cloud . For an estimation of what other products would cost, use Google’s Platform Pricing Calculator. . AWS has removed all ML barriers that have traditionally slowed down devs and data scientists. Innovative ML products and services on a well-known and widely trusted platform. Amazon Web Services .

Artificial Inteligence 78

Artificial Inteligence Machine Learning Azure Software Review 78

UruIT

JULY 23, 2019

Having these requirements in mind and based on our own experience developing ML applications, we want to share with you 10 interesting platforms for developing and deploying smart apps: Google Cloud. For an estimation of what its other products would cost, you can use Google’s Platform Pricing Calculator.

Artificial Inteligence 81

Artificial Inteligence Machine Learning Azure Software Review 81

Existek

SEPTEMBER 28, 2023

Monitoring and maintenance: After deployment, AI software developers monitor the performance of the AI system, address arising issues, and update the model as needed to adapt to changing data distributions or business requirements. The update with the latest trends and technologies in the AI field is also important.

Artificial Inteligence 52

Artificial Inteligence Development How To Artificial Intelligence 52

TIBCO - Connected Intelligence

APRIL 22, 2021

Opportunity 4: Migrate to the cloud. Leading cloud providers such as AWS, Microsoft Azure, and Google Cloud have developed world-class cloud data centers whose sustainability levels are difficult for organizations like yours to match because: They optimize server performance and usage elastically with demand, powering down what isn’t needed.

Sustainability 46

Sustainability Virtualization Data Energy 46

CIO

AUGUST 18, 2022

Moonfare, a private equity firm, is transitioning from a PostgreSQL-based data warehouse on AWS to a Dremio data lakehouse on AWS for business intelligence and predictive analytics. Users coming from a data warehouse environment shouldn’t care where the data resides,” says Angelo Slawik, data engineer at Moonfare.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

O'Reilly Media - Ideas

MARCH 6, 2019

Get hands-on training in machine learning, AWS, Kubernetes, Python, Java, and many other topics. An Introduction to Amazon Machine Learning on AWS , April 29-30. Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20.

Course 10

Course Artificial Inteligence Training Microservices 10

InfoQ Culture Methods

DECEMBER 28, 2020

As 2020 is coming to an end, we created this article listing some of the best posts published this year. This collection was hand-picked by nine InfoQ Editors recommending the greatest posts in their domain. It's a great piece to make sure you don't miss out on some of the InfoQ's best content.

Continuous Delivery 102

Continuous Delivery Google Cloud Data Engineering Microservices 102

O'Reilly Media - Ideas

MARCH 31, 2022

Looking a bit further into the difficulty of hiring for AI, we found that respondents with AI in production saw the most significant skills gaps in these areas: ML modeling and data science (45%), data engineering (43%), and maintaining a set of business use cases (40%). Use of AutoML tools. Deploying and Monitoring AI.

Enterprise 108

Enterprise Artificial Inteligence Government Budget 108

Cloudera

MAY 13, 2021

Fixed Reports / Data Engineering jobs . Often mission-critical to the various lines of business (risk analytics, platform support, or data engineering), which hydrate critical data pipelines for downstream consumption. Fixed Reports / Data Engineering Jobs. Data Engineering jobs only.

Data Engineering 68

Data Engineering Cloud Weak Development Team Resources 68

Cloudera

DECEMBER 15, 2022

With CDP, customers can deploy storage, compute, and access, all with the freedom offered by the cloud, avoiding vendor lock-in and taking advantage of best-of-breed solutions. The new capabilities of Apache Iceberg in CDP enable you to accelerate multi-cloud open lakehouse implementations. Enhanced multi-function analytics.

Cloud 76

Cloud Data Analytics Machine Learning 76

Perficient

FEBRUARY 16, 2024

Reading Data: # Reading data from DBFS val data_df = spark.read.csv("dbfs:/FileStore/tables/Largest_earthquakes_by_year.csv") The code will read the specified CSV file into a DataFrame named data_df, allowing further processing and analysis using Spark’s DataFrame API. Databricks on AWS

System 52

System Storage Azure Big Data 52

Altexsoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. What is Apache Airflow?

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems Review 52

DataRobot

SEPTEMBER 14, 2021

AI Cloud brings together any type of data, from any source, giving you a unique, global view of insights that drive your business. All of this is part of a unified, integrated platform spanning data engineering, machine learning, decision intelligence, and continuous AI – the entire AI lifecycle.

Cloud 52

Cloud Data Center Machine Learning Artificial Inteligence 52

Altexsoft

JULY 19, 2022

As the picture above clearly shows, organizations have data producers and operational data on the left side and data consumers and analytical data on the right side. Data producers lack ownership over the information they generate which means they are not in charge of its quality. It works like this.

Architecture 52

Architecture Data Analytics Data Engineering 52

Daniel Bryant

SEPTEMBER 13, 2019

What was worth noting was that (anecdotally) even engineers from large organisations were not looking for full workload portability (i.e. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8